Frequency-plane analysis of normal and pathological ECG signals for disease identification.
01 Sep 2005-Journal of Medical Engineering & Technology (Taylor & Francis)-Vol. 29, Iss: 5, pp 219-227
TL;DR: A frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification to show that amplitude property is significantly different for normal and Diseased subjects.
Abstract: In this paper a frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification. Image processing techniques are used to develop an automated data acquisition package of 12 lead ECG signals from paper records. A regeneration domain is also developed to check the captured pattern with the original wave shape. A QRS complex detector with an accuracy level ∼98.4% in up to 30% signal to noise level is developed. Discrete Fourier transform (DFT) is performed to obtain the frequency spectrum of every ECG signal. Some interesting amplitude and phase response properties of chest lead V2, V3, V4, V6 and limb lead I, II, III, AVL, AVF are seen. Both amplitude and phase properties are different for normal and diseased subjects and can serve an important role in disease identification. A statistical analysis of amplitude property is carried out to show that this property is significantly different for normal and diseased subjects.
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TL;DR: The proposed harmonic phase distribution pattern of the ECG data for MI identification provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification.
Abstract: Incorporation of automated electrocardiogram (ECG) analysis techniques in home monitoring applications can ensure early detection of myocardial infarction (MI), thus reducing the risk of mortality. Most of the published techniques use advanced signal processing tools, a huge number of ECG features, and complex classifiers, which make their hardware implementation difficult. This paper proposes the use of harmonic phase distribution pattern of the ECG data for MI identification. The morphological and temporal changes of the ECG waveform caused by the presence of MI are reflected in the phase distribution pattern of the Fourier harmonics. Two discriminative features, clearly reflecting these variations, are identified for each of the three standard ECG leads (II, III, and V2). Classification of the healthy and MI data is performed using a threshold-based classification rule and logistic regression. The proposed technique has achieved an average detection accuracy of 95.6% with sensitivity and specificity of 96.5% and 92.7%, respectively, for classifying all types of MI data from the Physionet Physikalisch-Technische Bundesanstalt diagnostic ECG database. The robustness of the algorithm is confirmed with real data as well. The algorithm is also implemented and validated on a microcontroller-based Arduino board, which can serve as a prototype ECG analysis device. Apart from providing comparable performance to other reported techniques, the proposed technique provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification.
54 citations
Cites methods from "Frequency-plane analysis of normal ..."
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01 Jan 2014
TL;DR: The precision of cardiac output algorithms are derived and evaluated to determine their precision during changes in vascular state, and indices of cardiovascular function which can be derived from these signals for detection of deteriorations are identified.
Abstract: Clinical deteriorations of hospital patients must be recognised early to maintain patient safety and minimise treatment costs. Currently early warning scores are calculated several times each day to warn of potential deteriorations. A score is calculated using parameters measured at one particular time. In contrast, clinicians often use physiological trends over time to improve their assessment. Therefore, we hypothesised that: Deterioration of inpatients could be detected earlier by monitoring their physiological trajectories. To test this hypothesis, we have constructed a database of patients’ physiology throughout their hospital stay after cardiac surgery, including continuous ECG and pulse oximetry signals. Data timestamps were misaligned during acquisition, so an algorithm has been developed to correct for this. Artefactual data was removed using signal quality indices. Initial physiological trajectories were calculated using Gaussian processes. We have implemented algorithms to estimate respiratory rate, a key indicator of deteriorations, from these signals. We have evaluated their precision in a cohort of healthy subjects. Preliminary results suggest that they are more precise in younger subjects. Therefore, further work is required to determine whether they are sufficiently precise for use with the patient population. We have identified indices of cardiovascular function which can be derived from these signals for detection of deteriorations. We hypothesised that the variability in cardiovascular state in the hours after surgery may indicate the class of trajectory which a patient is likely to follow. Therefore, we derived and evaluated the precision of cardiac output algorithms to determine their precision during changes in vascular state. Preliminary results suggest that more complex algorithms are required. Finally, we have identified the remaining steps required to test this hypothesis.
6 citations
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References
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TL;DR: 1. The numerical evaluation of expressions 2. Linear systems of equations 3. Interpolation and numerical differentiation 4. Numerical integration 5. Univariate non linear equations 6. Systems of nonlinear equations.
Abstract: Numerical analysis is an increasingly important link between pure mathematics and its application in science and technology. This textbook provides an introduction to the justification and development of constructive methods that provide sufficiently accurate approximations to the solution of numerical problems, and the analysis of the influence that errors in data, finite-precision calculations, and approximation formulas have on results, problem formulation and the choice of method. It also serves as an introduction to scientific programming in MATLAB, including many simple and difficult, theoretical and computational exercises. A unique feature of this book is the consequent development of interval analysis as a tool for rigorous computation and computer assisted proofs, along with the traditional material.
3,349 citations
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TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >
1,034 citations
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TL;DR: A new approach to ECG arrhythmia analysis is described, based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition.
Abstract: A new approach to ECG arrhythmia analysis is described. It is based on hidden Markov modeling (HMM), a technique successfully used since the mid 1970s to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The HMM approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum-likelihood reestimation algorithm. Initial results suggest that this approach can provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat, including the P-wave. >
486 citations
"Frequency-plane analysis of normal ..." refers methods in this paper
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TL;DR: AQRS complex detector based on the dyadic wavelet transform (D/sub y/WT) which is robust to time-varying QRS complex morphology and to noise is described which compared well with the standard techniques.
Abstract: In this paper, the authors describe a QRS complex detector based on the dyadic wavelet transform (D/sub y/WT) which is robust to time-varying QRS complex morphology and to noise. They design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. They illustrate the performance of the D/sub y/WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) database. Seventy hours of data was considered. The authors also compare the performance of D/sub y/WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results the authors observed that although no one algorithm exhibited superior performance in all situations, the D/sub y/WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the D/sub y/WT-based detector exhibited excellent performance.
433 citations
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TL;DR: This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure and a discussion of some of the current detection schemes is presented.
Abstract: The QRS detection algorithm is an essential part of any computer-based system for the analysis of ambulatory ECG recordings. This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure. A discussion of some of the current detection schemes is presented with regard to this structure. Some additional features of QRS detectors are mentioned. The evaluation of performance and the problem of multichannel detection, which is now gaining importance, are also briefly treated.
248 citations
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